curve-fitting

I was looking for a way to perform a linear curve fit in Javascript. I found several libraries, but they don't propagate errors. What I mean is, I have data and associated measurement errors, like: x = [ 1.0 +/- 0.1, 2.0 +/- 0.1, 3.1 +/- 0.2, 4.0 +/- 0.2 ] y = [ 2.1 +/- 0.2, 4.0 +/- 0.1, 5.8 +/- 0.4, 8.0 +/- 0.1 ] Where my notation a +/- b means { value : a, error : b } . I want to fit this into y = mx + b , and find m and b with their propagated errors. I know the Least Square Method algorithm, that I could implement, but it only take errors on the y variable, and I have distinct errors in

I am currently using numpy.polyfit(x,y,deg) to fit a polynomial to experimental data. I would however like to fit a polynomial that uses weighting based on the errors of the points. I have found scipy.curve_fit which makes use of weights and I suppose I could just set the function, 'f', to the form a polynomial of my desired order, and put my weights in 'sigma', which should achieve my goal. I was wondering is there another, better way of doing this? Many Thanks. Bernhard Take a look at http://scipy-cookbook.readthedocs.io/items/FittingData.html in particular the section 'Fitting a power-law

I don't understand curve_fit isn't able to estimate the covariance of the parameter, thus raising the OptimizeWarning below. The following MCVE explains my problem: MCVE python snippet from scipy.optimize import curve_fit func = lambda x, a: a * x popt, pcov = curve_fit(f = func, xdata = [1], ydata = [1]) print(popt, pcov) Output \python-3.4.4\lib\site-packages\scipy\optimize\minpack.py:715: OptimizeWarning: Covariance of the parameters could not be estimated category=OptimizeWarning) [ 1.] [[ inf]] For a = 1 the function fits xdata and ydata exactly. Why isn't the error/variance 0 , or

I was able to fit curves to a x/y dataset using peak-o-mat , as shown below. Thats a linear background and 10 lorentzian curves. Since I need to fit many similar curves I wrote a scripted fitting routine, using mpfit.py , which is a Levenberg-Marquardt-Algorithm. However the fit takes longer and, in my opinion, is less accurate than the peak-o-mat result: Starting values Fit result with fixed linear background (values for linear background taken from the peak-o-mat result) Fit result with all variables free I believe the starting values are already very close, but even with the fixed linear

I have some measurements done and It should be a damped sine wave but I can't find any information on how to make (if possible) a good damped sine wave with Matlab's curve fitting tool. Here's what I get using a "Smoothing spline": Image http://s21.postimg.org/yznumla1h/damped.png . Edit 1 : Here's what I got using the "custom equation" option: Edit 2 : I've uploaded the data to pastebin in csv format where the first column is the amplitude and the second is the time. The damped sin function can be created using the following code: f=f*2*pi; t=0:.001:1; y=A*sin(f*t + phi).*exp(-a*t); plot(t,y)

I have a data set and a kernel density estimate for those data. I believe the KDE should be reasonably well described by an exponentinally modified Gaussian , so I'm trying to sample from the KDE and fit those samples with a function of that type. However, when I try to fit using scipy.optimize.curve_fit, my fit doesn't match the data well at all. My code is import scipy.special as sse from scipy.optimize import curve_fit def fit_func(x, l, s, m): return 0.5*l*n.exp(0.5*l*(2*m+l*s*s-2*x))*sse.erfc((m+l*s*s-x)/(n.sqrt(2)*s)) # exponential gaussian popt, pcov = curve_fit(fit_func, n.linspace(0,1